Combinatorial and machine learning approaches in clustering microarray data

In this paper we describe the use of a correlation clustering algorithm [Chaitanya, 2004] to group expression level of genes in a microarray dataset. The clustering problem is formalized as a semi-defined optimization program, based on the correlation provided by two quantities, respectively related to an agreement and a disagreement between a pair of genes. We also intend to validate the role of the correlation clustering algorithm by comparing the results with a support vectors clustering approach [Ben-Hur et al., 2001] that is demonstrated to perform well for many applications.